---
title: "Dashboard"
output: 
  flexdashboard::flex_dashboard:
    orientation: columns
    vertical_layout: fill
    source_code: embed
    
---

```{r setup, message = FALSE, echo = FALSE}
library(flexdashboard)
library(tidyverse)
library(p8105.datasets)
library(plotly)
library(readxl)
```

```{r, include=FALSE}
ghed_df <- 
  read_excel("./data/GHED_data.XLSX")

ghed_df<-ghed_df %>% 
    janitor::clean_names() 

developed_nations<-ghed_df%>%
  filter(country=="Canada"| country=="Japan"| country=="Republic of Korea"| country=="France"| country=="Germany"| country=="Italy"| country=="United Kingdom"| country=="United States of America")

undeveloped_nations <-ghed_df%>%
  filter(country!="Canada" & country!="Japan"& country!="Republic of Korea"& country!="France" & country!="Germany"& country!="Italy"& country!="United Kingdom"& country!="United States of America", year == 2019)
```

Column {data-width=500}
-----------------------------------------------------------------------

### Private vs. Government Expenditure in Developed Countries in 2019
```{r}
ghed_df%>%
  filter(country=="Canada"| country=="Japan"| country=="Republic of Korea"| country=="France"| country=="Germany"| country=="Italy"| country=="United Kingdom"| country=="United States of America")%>%
  filter(year == 2019)%>%
  select(country, year, gghed_che, pvtd_che)%>%
  mutate(sum_per = gghed_che + pvtd_che)%>%
  pivot_longer(cols = contains("che"),
               names_to = "exp_type",
               values_to = "exp_per")%>%
  mutate(exp_type = replace(exp_type, exp_type == "gghed_che", "Government Expenditure"),
         exp_type = replace(exp_type, exp_type == "pvtd_che", "Private Expenditure"))%>%
  plot_ly(x=~country, y=~exp_per, name=~exp_type, color =~exp_type, type = 'bar', colors = "viridis")%>%
  layout(yaxis = list(title = 'Percent of Total Expenditure'), xaxis = list(title = "Country", categoryorder = "total descending"), barmode = 'stack', colors = "viridis")

```

Column {data-width=500}
-----------------------------------------------------------------------
### Out-of-pocket spending as % of Current Health Expenditure
```{r}
developed_nations %>%
  filter(year==2019) %>%
  select(country, oops_che) %>%
  arrange(desc(oops_che)) %>%
  plot_ly(x=~country, y=~oops_che, name=~country, color =~country, type = 'bar', colors = "viridis")%>%
  add_trace(data=undeveloped_nations, x=~country, y=~oops_che, name=~country, color =~country, type = 'bar', colors = "viridis", visible="legendonly") %>% 
  layout(yaxis = list(title = 'Out-of-pocket (OOPS) as % of Current Health Expenditure'), xaxis = list(title = "Country"), barmode = 'stack', colors = "viridis")%>%
  layout(xaxis = list(categoryorder = "total descending")) 
```
### Social Health Insurance as % of Current Health Expenditure
```{r}
developed_nations%>%
  filter(year==2019)%>%
  select(country, shi_che)%>%
  arrange(desc(shi_che))%>%
  plot_ly(x=~country, y=~shi_che, name=~country, color =~country, type = 'bar', colors = "viridis")%>%
    add_trace(data=undeveloped_nations, x=~country, y = ~shi_che, name=~country, color =~country, type = 'bar', colors = "viridis", visible="legendonly") %>% 
  layout(yaxis = list(title = 'Social Health Insurance (SHI) as % of Current Health Expenditure (CHE)'), xaxis = list(title = "Country"), barmode = 'stack', colors = "viridis")%>%
  layout(xaxis = list(categoryorder = "total descending"))
```